Zonal redundancy in API management Standard v2
APIs are the backbone of modern applications, powering everything from mobile experiences and microservices to AI-driven applications and business-critical integrations. As customers continue to modernize their platforms on Azure, they increasingly expect their API infrastructure to remain available even in the face of datacenter-level disruptions. With zone redundancy in Standard v2, Azure API Management now enables customers to increase resilience against Availability Zone failures while continuing to benefit from the simplicity, performance, and cost efficiency of the v2 platform. Why Zone Redundancy Matters Azure Availability Zones are physically separate locations within an Azure region, each with independent power, cooling, and networking infrastructure. By distributing API Management resources across multiple zones, organizations can reduce the impact of a single datacenter failure and improve service continuity for their APIs. Until now, customers who required built-in zone-level resiliency often needed to evaluate higher-end deployment options. With this enhancement, Standard v2 customers can now deploy API gateways across Availability Zones and benefit from improved reliability while maintaining the streamlined operational model of the v2 platform. What’s New Zone Redundancy for Standard v2 extends the platform's resiliency by distributing service capacity across multiple Availability Zones within a supported Azure region. Key benefits include: Higher Availability: API traffic continues to flow even if a single Availability Zone experiences an outage. Built-in Resiliency: Redundancy is provided at the platform layer, reducing the need for customers to design and manage complex intra-region failover solutions. Production-Ready Reliability: Customers can confidently run critical API workloads on Standard v2 with stronger availability guarantees. Operational Simplicity: The service automatically manages capacity distribution, health monitoring, and recovery behavior across zones. Cost-Effective Resilience: Customers gain zone-level protection without requiring an enterprise-tier deployment model. Built on the Modern v2 Platform The v2 platform was designed from the ground up to provide a faster, more reliable, and more scalable API Management experience. Standard v2 already delivers capabilities such as rapid deployment, simplified networking, workspace support, and flexible scaling. Zone Redundancy further strengthens the platform by expanding its reliability story for production workloads. This announcement builds on our broader investment in making Azure API Management more accessible to a wider range of organizations, from digital-native startups to large enterprises modernizing their application estates. Ideal Scenarios Zone Redundancy in Standard v2 is particularly valuable for customers who: Run business-critical APIs that must remain available during datacenter incidents. Consolidate multiple application workloads behind a single API gateway. Expose APIs consumed by mobile, partner, and customer-facing applications. Support AI applications and agent-based architectures that depend on highly available API endpoints. For organizations adopting modern cloud and AI native architectures, this capability helps ensure that API infrastructure remains aligned with broader application resiliency strategies. A Foundation for Reliable AI and API Platforms As AI-powered applications continue to proliferate, APIs increasingly become the critical connection layer between models, agents, business systems, and data platforms. Downtime at the API layer can have a direct impact on application availability, customer experience, and business operations. By bringing zone redundancy to Standard v2, we are making it easier for organizations to build highly resilient API platforms that can serve as the foundation for next-generation AI and digital transformation initiatives. Getting Started Zone Redundancy for Standard v2 can be enabled in supported Azure regions, allowing customers to deploy API Management with built-in protection against Availability Zone failures. We recommend reviewing your application's overall resiliency architecture, including backend redundancy, traffic management, and disaster recovery requirements, to maximize the benefits of zone-resilient API infrastructure. Enable Zone Redundancy in the Azure Portal Getting started with Zone Redundancy in Azure API Management Standard v2 is straightforward and can be configured during service creation. Create a New Standard v2 Instance with Zone Redundancy Sign in to the Azure portal. Select Create a Resource and search for Azure API Management. Choose Standard v2 as the service tier. Select a region that supports Availability Zones. In the Availability Zones section, enable Zone Redundancy. Review and create the service. After deployment, Azure API Management automatically distributes service capacity across multiple Availability Zones within the selected region, helping maintain API availability during a zone-level outage. Looking Ahead This release represents another step in our ongoing investment in the Azure API Management v2 platform. We remain committed to delivering the reliability, scalability, security, and developer experiences that organizations expect from a modern API management service. We are excited to see what our customers build with a more resilient Standard v2 platform and look forward to your feedback as you continue modernizing and scaling your API ecosystems on Azure. Learn more by visiting the Azure API Management documentation and exploring the latest reliability guidance for API Management deployments.The AI Trust Gap: We're Auditing Outputs, But Nobody's Watching the Input
Hi all, Manjish here, founder of Pryvasee.AI. I want to open a conversation rather than make a pitch, because I think this is a problem bigger than any one company can solve, and I'd like to hear how others in this community are approaching it. Over the past year, watching enterprises adopt LLMs like ChatGPT, Claude, Gemini, Grok, DeepSeek, etc one pattern kept showing up. Almost every governance conversation started after the prompt was sent. Teams were building dashboards to review AI outputs, running periodic audits, writing acceptable use policies. All useful, but all reactive. Nobody I spoke to had a clear answer to a much simpler question: what actually happens to the sensitive data in that prompt in the moments before it leaves your organisation's control? That gap is why we built Pryvasee.AI differently. Instead of sitting after the model and reviewing what came back, we sit before it. Pryvasee Guard screens prompts, documents, and images for PII, PHI, and PCI and other such sensitive data before anything reaches a model. Pryvasee Thread lets you run the same request across OpenAI, Gemini, Grok, and DeepSeek from one interface, so you're never trusting a single model's answer by default. And the Trust Engine scores every response that comes back for groundedness and hallucination risk, so there's a number behind "does this look right" instead of a gut feeling. We built it natively on Azure (AKS, Azure SQL, Azure SQL Ledger for a tamper evident audit trail) because we think the next wave of AI governance problems won't just be about data leakage. They will be about proving, after the fact, exactly what happened, for a regulator, an auditor, or your own board. Most organisations can't do that today for a single AI interaction, let alone thousands a day across four different model providers. We're early. MVP/Beta since July 2026, live on the Microsoft Commercial Marketplace since late August, and currently working through a handful of enterprise pilots rather than claiming a long customer list. I would rather be upfront about that than oversell it. What I'm genuinely curious about: for those of you building or advising on enterprise AI adoption, is anyone handling the "before the model" problem today, whether with tooling, policy, or something else? And do you think this becomes a bigger issue as more employees start using multiple AI tools side by side, or does it resolve itself as the big model providers add more guardrails natively? Would love to hear how others are thinking about this.AI Skills Navigator is now available in Microsoft Copilot
Start with a question for the Learning Agent in Copilot and get recommendations from AI Skills Navigator, helping you find the right next step whenever you need to learn something new. Matt Erni is Product Manager at Microsoft working across Learning Agent and AI Skills Navigator to bring relevant skilling into the flow of work. Learning new AI skills has never been more important for individuals and organizations alike. But when you need to learn something new, finding the right next step isn’t always straightforward. Let’s say you’re preparing a presentation, building your first agent, securing an AI workload, or even planning your next career step. You’re right in the middle of a task and realize there’s a skill you need to learn to do it well. The challenge isn't just finding training. It's finding resources you can trust that are tailored to your role, goals, experience level, and preferred way to learn. That’s why we recently introduced Learning Agent in Microsoft Copilot, a personalized AI upskilling experience that helps you build Copilot and AI skills in the flow of work. Learning Agent uses role information, work context, skills signals, and organizational learning sources to bring personalized recommendations directly into Copilot, helping you discover relevant learning opportunities and build skills when and where you need them most. Today, we're extending that experience through a new integration with AI Skills Navigator, our agentic skilling platform. Learning Agent can now also recommend training, skilling experiences, and credentials from AI Skills Navigator directly within Copilot, making it easier to move from a question to the next step in building a skill. Videoclip: Overview of Learning Agent in Microsoft Copilot. If the player doesn’t load, open the video in a new window. Turn work questions into learning opportunities Whether you're trying to solve a problem, build a new skill, or explore your next learning goal, you can start by asking a question in Copilot using natural language. Here are some examples: How can I use Copilot in new ways? What should I learn before building my first agent? Which videos can help me learn about Copilot Cowork? What courses can help me learn how to secure my AI agent? Which Microsoft credential can help me lead AI adoption in my organization? Learning Agent uses your question along with context from you and your organization to surface personalized skilling recommendations and guidance. With the AI Skills Navigator integration, those recommendations can now also include training modules, learning paths, skilling sessions, videos, and Microsoft Credentials. So, no matter what you're trying to accomplish, Learning Agent is there to guide you to the right next step. The recommendations from AI Skills Navigator are clearly labeled, so you can understand the source and choose the next step that works for you. Getting started is simple: open Learning Agent in Microsoft Copilot and ask a question about a skill, topic, or credential you'd like to learn more about. Keep learning without losing your place When Learning Agent recommends a module, learning path, or curated video from AI Skills Navigator, you keep the context that brought you there. You can open the recommendation alongside your Copilot conversation, explore the content, and use features such as AI-generated summaries and podcasts as you go. Progress is automatically saved in AI Skills Navigator, making it easy to pick up where you left off and continue learning over time. There's no separate sign-in required. The full AI Skills Navigator catalog gives you access to interactive learning experiences like skilling sessions or opportunities to earn Microsoft Credentials. The first time you access these experiences, you'll create an AI Skills Navigator profile, unlocking additional personalized learning, progress tracking, and recommendations in the full experience. Whether you start with a quick recommendation or continue into a skilling session or credential, you can keep learning without losing momentum. Learning for individuals—and entire organizations Learning Agent and AI Skills Navigator are designed to support you in skilling at any level while also helping your organization scale learning more effectively. Learning Agent can surface recommendations from organizational knowledge sources, including SharePoint, LinkedIn Learning, learning management systems, third-party content, role-play providers, and now AI Skills Navigator. This helps employees spend less time searching for learning resources and more time building skills. So, whether you’re building AI fluency, preparing for a new role, earning a credential, or developing technical expertise, Learning Agent and AI Skills Navigator help connect everyday questions with the learning opportunities that matter most. At the organizational level, this creates a stronger connection between employee learning, company knowledge, and the skills needed to support business priorities. Availability: Learning Agent is available to organizations and their employees with Microsoft Copilot. Access to connected learning experiences, including some third-party sources, depends on how an organization has configured and licensed those sources. Check with your organization if you're not sure what’s available to you. Keep building skills over time Learning Agent helps you get started on the right track. AI Skills Navigator helps you go deeper, track progress, and keep building skills over time. Together, they help individuals and organizations connect learning to real work and skilling goals. Get started now. Add Learning Agent to Microsoft Copilot and start asking questions about the skills, topics, and credentials you’d like to learn more about. New to AI Skills Navigator? Read The moment AI skilling stopped being optional—and started being personal.Discover how Dragon Copilot solutions can reach clinicians through Microsoft Marketplace
As healthcare organizations adopt AI to streamline care delivery, partners have a new opportunity to bring innovation directly into clinical workflows. Microsoft Marketplace now supports Dragon Copilot Physician Apps and Agents, helping partners deliver intelligent solutions that support documentation, clinical insights, and workflow efficiency at the point of care. Learn how Microsoft Marketplace helps partners publish, monetize, and scale Dragon Copilot solutions through flexible commercial models, trusted purchasing experiences, and targeted healthcare discovery experiences. Read the article to explore the new Dragon Copilot offer type and how to bring your healthcare innovation to market through Microsoft Marketplace. 👉 Read the full article: Accelerate healthcare innovation with Dragon Copilot apps and agents in Microsoft Marketplace
"People section" turning on by itself and digging into my old photos without permission
I have used OneDrive on Android for years and just now I had a notification appear with an old picture of someone in my family asking me to type in her name and if other pictures of her are also her. I was shocked and irritated at this apparent privacy violation. I found out a "feature" called "People section" where "OneDrive uses AI to recognize faces in your photos" just turned itself on and decided to send me a notification. How utterly irritating. Microsoft should understand how unnerving it is to have your phone seem to just start digging into your photos without your permission. Why couldn't it just have a notification asking me if I want to turn on the feature and explain what it is? Why show my personal family pictures?
Accelerate healthcare innovation with Dragon Copilot apps and agents in Microsoft Marketplace
Imagine spending less time navigating systems and more time caring for patients. AI brings data, insights, and content directly into the flow of work. Dragon Copilot streamlines clinical documentation and routine tasks, so clinicians spend less time navigating systems and more time focused on patient care. By simplifying physician and nursing charting, notes, flowsheets, and radiology reporting, it reduces rework and cognitive burden, helping care teams work more efficiently and confidently throughout the day. Microsoft Marketplace now supports Dragon Copilot AI Apps and Agents, creating a scalable path for partners to bring innovation directly into clinical workflows. Dragon Copilot unifies intelligence and context at the point of care, while Microsoft Marketplace is how partner innovation becomes part of that experience, enabling AI apps and agents to operate within the trusted procurement, security, and governance frameworks customers already rely on. Partners building AI apps and agents for Dragon Copilot can use Microsoft Marketplace as their commercial engine. Marketplace supports multiple enterprise‑ready sales motions, whether selling direct, or through partners for channel-led sales. This gives partners flexibility in how they go to market and meet customers where they prefer to buy. With support for per‑user and flat‑rate pricing, flexible billing options, and contract terms ranging from monthly to multi‑year, Marketplace enables partners to align their commercial model to customer procurement needs while scaling globally through trusted Microsoft purchasing, security, and global commerce. Dragon Copilot offer types in Microsoft Marketplace Microsoft Marketplace will support a family of Dragon Copilot offer types designed for partner solutions across clinical roles and workflows. The first offer type - Dragon Copilot Physician Apps and Agents - is now available, enabling partners to publish solutions that surface directly within physician workflows. Each offer type defines the intended user, where the solution appears in Dragon Copilot, and how customers discover, purchase, and deploy it, making offer selection a strategic decision that shapes monetization and the end-to-end customer experience. Supported offer type Dragon Copilot Physician Apps and Agents AI solutions that will surface directly inside Dragon Copilot at the physician point of care. They deliver in‑workflow access to insights, task automation, clinical decision support, and documentation assistance without requiring clinicians to leave their existing tools. These solutions are purchased through Microsoft Marketplace and become automatically available within Dragon Copilot after deployment. (Available in the United States) Best for: AI apps or agents built specifically for physician workflows Partners delivering role‑based intelligence Scenarios where fast, in‑context action inside Dragon Copilot is critical How it works Bringing an app or agent to Dragon Copilot through Microsoft Marketplace is designed to be simple and enterprise‑ready. Marketplace provides the commercial, operational, and discovery foundation so partners can focus on building differentiated AI experiences. Healthcare organizations can also choose to build and deploy apps or agents in Dragon Copilot and expand usage through the Marketplace. Step 1: Create offer selecting the offer type Begin in Partner Center by choosing the Dragon Copilot offer type that best aligns to your solution and target clinical persona. This choice determines where your solution surfaces inside Dragon Copilot, which roles it supports, and how customers discover, purchase, and activate it. After choosing the offer type, define pricing, billing, contract terms, and sales options. Microsoft Marketplace offers flexible pricing, sales channels, anc contract lengths to meet healthcare organizations' needs. Step 2: Customer discovery After publishing, customers can discover and purchase Dragon Copilot AI apps and agents through Microsoft Marketplace and through in-product experiences like, Azure. Step 3: Customer purchase Customers purchase Dragon Copilot solutions through the Azure portal using the same streamlined experience they use for other offers on Microsoft Marketplace. Customers complete their subscription details, and billing begins once they configure their account. From there, they are redirected to the Dragon Admin Center to complete a few final configuration steps and begin using the solution. Step 4: Customer usage After purchase, customers are guided to the Dragon Admin Center, where they activate and manage their Dragon Copilot solutions. From the admin experience, customers can complete any required configuration, assign the app or agent to the appropriate users or roles, and control how it is made available within Dragon Copilot workflows. The Dragon Admin Center provides a centralized place for customers to enable partner solutions, manage access, and ensure deployments align with their security, governance, and operational policies so AI apps and agents are ready for use at the point of care with minimal setup. What this means for you As Dragon Copilot adoption accelerates, Microsoft Marketplace provides a clear, scalable foundation for partner participation. With a dedicated Dragon Copilot offer type, partners can publish AI apps, agents, through a defined commercial model, gain targeted visibility through Dragon Copilot–specific discovery, and take advantage of Microsoft go‑to‑market programs. Learn more Read through our documentation on how extensions for Dragon Copilot work and how to build your own - AI apps and agents | Microsoft Learn Check out the sample repo with sample code and more - microsoft/dragon-copilot-extension-samplesPartner Blog | Building the foundation for AI: Cloud, data, security, and AI skills for partners
Customers are moving beyond AI experimentation. They are looking for partners who can connect AI ambition to the cloud, data, security, governance, and business application capabilities required to put AI to work. That makes skilling across the Microsoft stack increasingly important. It can also make the question of where to start more difficult. This month, there is a simpler starting point. The Microsoft Partner Skilling Hub Agent can recommend training, answer skilling-related questions, and generate personalized technical skilling plans based on your role and goals. From there, you can build a focused path across Frontier Transformation, agents, Microsoft 365 Copilot, certifications, hands-on learning, and co-sell execution. The foundation for AI is broader than AI skills Frontier Transformation is the shift from targeted AI pilots to repeatable, governed AI capabilities embedded into the flow of work, business processes, and customer engagement. For partners, delivering that transformation requires more than expertise in a single AI product. It requires teams that understand how cloud infrastructure, data, security, agents, and business applications work together. That foundation matters across customer segments. For partners serving small and medium-sized businesses (SMBs), it can support you in guiding customers toward practical AI adoption while addressing security, governance, productivity, and business process needs together. This month, focus your skilling plan on five areas: validating your technical expertise, building agent platform capabilities, developing AI business application skills, earning industry-recognized certifications, and applying those skills through hands-on learning. Continue reading hereCopilot, Microsoft 365 & Power Platform Community call
💡 Copilot, Microsoft 365 & Power Platform weekly community call focuses on different use cases and features within the Microsoft 365 and Power Platform - across Microsoft 365 Copilot, Copilot Studio, SharePoint, Power Apps and more. Demos in this call are presented by the community members 🙏 👏 Looking to catch up on the latest news and updates, including cool community demos, this call is for you! 📅 On 27th of August we'll have following agenda: Copilot prompt of the week CommunityDays.org update Microsoft 365 Maturity model PnP Framework and Core SDK extension PnP PowerShell Script samples Copilot pro dev samples Power Platform samples Lee Ford & Reshmee Auckloo– Multi-agent patterns in M365 Copilot Sriram Balaji – Using Skills in Copilot Studio New Experience Nathalie Leenders – How to get Usage metrics from the Power Platform Admin Center? 📅 Download recurrent invite from https://aka.ms/community/m365-powerplat-dev-call-invite 📞 & 📺 Join the Microsoft Teams meeting live at https://aka.ms/community/m365-powerplat-dev-call-join 💡 Building something cool for Copilot, Microsoft 365 or Power Platform (Copilot Studio, SharePoint, Power Apps, etc)? We are always looking for presenters - Volunteer for a community call demo at https://aka.ms/community/request/demo 👋 See you in the call! 📖 Resources: Previous community call recordings and demos from the Microsoft Community Learning YouTube channel at https://aka.ms/community/youtube Microsoft 365 & Power Platform samples from Microsoft and community - https://aka.ms/community/samples Microsoft 365 & Power Platform community details - https://aka.ms/community/home 🧡 Sharing is caring!Copilot, Microsoft 365 & Power Platform product updates call
💡Copilot, Microsoft 365 & Power Platform product updates call concentrates on the different use cases and features within the Microsoft 365 and in Power Platform. Call includes topics like Microsoft 365 Copilot, Copilot Studio, Microsoft Teams, Power Platform, Microsoft Graph, Microsoft Viva, Microsoft Search, Microsoft Lists, SharePoint, Power Automate, Power Apps and more. 👏 Weekly Tuesday call is for all community members to see Microsoft PMs, engineering and Cloud Advocates showcasing the art of possible with Microsoft 365 and Power Platform. 📅 On the 18th of August we'll have following agenda: News and updates from Microsoft Together mode group photo Ed Williams – Bringing the physical world to Copilot Studio Adam Wójcik – Setup and use PnP PowerShell with Copilot to manage your tenant without knowing it Vesa Juvonen – Building Copilot Apps with React – Employee HR Agent Scenario 📞 & 📺 Join the Microsoft Teams meeting live at https://aka.ms/community/ms-speakers-call-join 🗓️ Download recurrent invite for this weekly call from https://aka.ms/community/ms-speakers-call-invite 👋 See you in the call! 💡 Building something cool for Microsoft 365 or Power Platform (Copilot, SharePoint, Power Apps, etc)? We are always looking for presenters - Volunteer for a community call demo at https://aka.ms/community/request/demo 📖 Resources: Previous community call recordings and demos from the Microsoft Community Learning YouTube channel at https://aka.ms/community/youtube Microsoft 365 & Power Platform samples from Microsoft and community - https://aka.ms/community/samples Microsoft 365 & Power Platform community details - https://aka.ms/community/home 🧡 Sharing is caring!Model router updates: new regions, a refreshed model pool, and understanding the hill climb
Across Microsoft, "hill climbing" has become shorthand for how real AI progress happens: not in one dramatic leap, but through a disciplined loop. Microsoft AI defines the hill climb as an organization that continuously improves, cycle after cycle, through more compute, better data, and sharper evaluation. Reinforcement fine-tuning in Foundry defines it as improving the deployable model package one measured step at a time across quality, latency, and cost. Different altitudes, same premise: progress is not a one-shot decision. It's a loop. For most teams, the decision of what model to use when is made manually or with custom routing tools. A developer picks a model based on benchmarks, familiarity, or the last launch that made headlines, ships it, and revisits the choice only when something breaks. In an ecosystem where the frontier moves monthly, that decision goes stale fast. Model router in Foundry Models brings the hill climb to the selection layer. What's new: a bigger pool, in more places This release expands where teams can deploy model router, broaden the supported model pool, and delivers updates through a stable endpoint. Together, these changes help teams run production workloads in more locations, match a wider range of tasks to suitable models, and adopt supported updates without changing the application integration. A refreshed model pool. The supported model list now includes Anthropic Claude Opus 4.8 — a high-capability model built for complex reasoning and long-form generation, for scenarios that demand depth, structure, and quality — and the GPT-5.6 family. Just as importantly, the pool is pruned: gpt-5-chat, gpt-5.2-chat, gpt-5.3-chat, Deepseek-V3.1 have been removed from the model router as models reach the end of their lifecycle and are deprecated in Foundry. New region availability. The model router is now available in 28 regions for global standard and 21 data zone regions. For many organizations, inference requests must stay within specific geographic boundaries for regulatory, governance, or customer-trust reasons — and intelligent routing shouldn't force a compromise on that. Find the full list of regions here. The most important detail is what you don't have to do: these updates occur automatically*. The endpoint remains stable as the supported model pool is refreshed, so teams do not need to redeploy the model router to receive the update. Applications can continue using the same integration while the model router evaluates requests against the current supported pool. Teams should continue monitoring routing traces and application outcomes to confirm that quality, cost, latency, and governance requirements are met. *Models from Anthropic still need to be deployed separately before they can be routed to through the model router. Interested in hearing more about what's new to the model router? Tune in for the next episode of Model Mondays with Sanjeev Jagtap and Lee Stott, where they talk all things model router from evaluations to hill climbing. Sign up here to watch live or view the replay: Model Mondays - Spotlight On Model router in Microsoft Foundry | Microsoft Reactor The selection-layer hill climb At the selection layer, a step is a routing decision. Each one is a micro-optimization against your objective, and each one is instrumented: every response from the model router includes a model field showing which underlying model was selected, so the climb leaves a complete, auditable trail. Model router supports three parts of the optimization loop: A/B testing to compare two router configurations to understand quality, cost, and latency tradeoffs; model decomposition to use routing results to decompose a single-model application into a multi-model or multi-agent design, and continuous routing to keep the router in production for continuous per-request selection. Each pattern turns model choice into a measured, repeatable process rather than a fixed decision. 1. A/B Testing Question: Which model or routing strategy should I use in production? A/B testing helps teams compare candidate models, model families, or router configurations against the same workload. Representative traffic is sent to competing deployments, and teams compare quality, cost, latency, and governance outcomes. The goal is to understand tradeoffs and identify the model or routing strategy that best meets workload requirements before promoting it to production. 2. Model Decomposition Question: What work is my application actually doing? Model decomposition uses model router as a diagnostic tool. By deploying the model router against a representative workload and examining routing telemetry, teams can see how requests naturally separate into different task classes. Simple retrieval, classification, and summarization requests may route to smaller models, while reasoning, planning, and agentic workflows may require more capable models. The goal is not to choose a winner, but to understand the structure of the workload and uncover opportunities for optimization, specialization, or architectural improvements. 3. Route continuously Question: Why choose a single model at all? Route continuously is the pattern model router was designed for but is not limited to. Rather than treating model selection as a one-time decision, teams leave the model router in production and allow the best-fit model to be selected for each request. As the supported model pool, regional availability, and platform capabilities evolve, teams can continue using the same endpoint while evaluating whether updates improve workload outcomes. Model selection becomes an ongoing optimization process rather than a project that must be repeated every time the model landscape changes. Together, these patterns illustrate a broader shift: the model router is more than a model. It is a tool for the optimization loop itself, helping teams evaluate tradeoffs, understand workload behavior, test hypotheses, and continuously refine model selection as requirements evolve. Whether used to compare candidate models, decompose applications into specialized tasks, or automate per-request routing in production, model router turns model selection into an observable, measurable, and repeatable process. As the model landscape continues to change, that optimization loop becomes a durable advantage. Getting Started Ready to start your own hill climb? Whether you're exploring the model router for the first time, evaluating routing strategies against your workload, or building a long-term optimization practice, these resources can help you move from experimentation to production with Microsoft Foundry. What's new in model router? Sign up for the next Model Mondays episode for a deep dive into new features, optimization patterns, and the latest model router updates. How do I build agents with model router? Check out the Model Router Agents Lab and build agent experiences with routing, retrieval, web search, tool calling, and multi-agent patterns. How do I evaluate model router? Compare model router against baseline models using your own prompts, then review quality, cost, latency, and routing decisions with the Auto Evaluation Toolkit. How do I optimize model router for my workload? Start your hill-climbing journey with the Model Mastery workshop, where you'll test one optimization lever at a time and measure how each change impacts workload outcomes. How do I build a model router optimization playbook? Explore the Model Releases repository to track new capabilities, understand the optimization question behind each release, and try focused notebooks that demonstrate one optimization lever at a time.
